System for determining local intent in a search query

ABSTRACT

A system and method are disclosed for determining local intent. Local intent may reflect whether a search query should receive results and advertisements that are geographically specific. The local intent may be determined using probabilistic models that analyze historical searches to determine which search terms tend to have local intent.

RELATED APPLICATIONS

The present application relates to applications entitled “SYSTEM ANDMETHOD FOR ASSOCIATING A GEOGRAPHIC LOCATION WITH AN INTERNET PROTOCOLADDRESS,” filed on Mar. 28, 2007, U.S. patent application Ser. No.11/729,365; “SYSTEM AND METHOD FOR ASSOCIATING A GEOGRAPHIC LOCATIONWITH AN INTERNET PROTOCOL ADDRESS,” filed on Mar. 28, 2007, U.S. patentapplication Ser. No. 11/729,364; “SYSTEM AND METHOD FOR ASSOCIATING AGEOGRAPHIC LOCATION WITH AN INTERNET PROTOCOL ADDRESS,” filed on Mar.28, 2007, new U.S. patent application Ser. No. 11/729,377; “SYSTEM FORDETERMINING THE GEOGRAPHIC RANGE OF LOCAL INTENT IN A SEARCH QUERY,”filed on Mar. 28, 2007, U.S. patent application Ser. No. 11/729,104;“SYSTEM FOR PROVIDING GEOGRAPHICALLY RELEVANT CONTENT TO A SEARCH QUERYWITH LOCAL INTENT,” filed on Mar. 28, 2007, new U.S. patent applicationSer. No. 11/729,103; each of which is incorporated by reference.

BACKGROUND

Online advertising may be an important source of revenue for enterprisesengaged in electronic commerce. A number of different kinds of web pagebased online advertisements are currently in use, along with variousassociated distribution requirements, advertising metrics, and pricingmechanisms. Processes associated with technologies such as HypertextMarkup Language (HTML) and Hypertext Transfer Protocol (HTTP) enable aweb page to be configured to contain a location for inclusion of anadvertisement. A page may not only be a web page, but any otherelectronically created page or document. An advertisement can beselected for display each time the page is requested, for example, by abrowser or server application.

Online advertising may be linked to online searching. Online searchingis a very common way for consumers to locate information, goods, orservices on the Internet. A consumer may use an online search engine totype in one or more keywords to search for other pages or web sites withinformation related to the keyword(s). The advertising that is shown onthe search engine page may be related to the keyword(s). In particular,a search results page may be displayed, which may include the searchresults, as well as advertisements, related to the keyword(s) thatproduced the search results.

The advertisements related to search results shown as a search enginepage, or based on content from other pages may be targeted to theconsumer viewing the page. In particular, advertisers would like fortheir advertisements to be shown to those consumers who would be mostlikely to select the advertisement and to view the advertiser's page, orpurchase the advertiser's goods or services. Accordingly, theadvertising provider, such as a search engine, may attempt to determinethe intent of consumers when those consumers view or interact with a webpage.

Consumers use the Internet and search engines to find information andmake decisions among online entities such as websites, online companies,or online services, independent of geographic constraints. For example,online retailers may provide goods or services to any location in theUnited States. Accordingly, contextual relevancy plays a role in drivingeconomic value by helping consumers make decisions in their onlinelives. For example, the Internet may be used to help consumers finduseful online services, online merchants and online information. Inaddition, the Internet is evolving into a type of informational utilitythat helps consumers make important local or geographic-specificdecisions in their offline lives as well. Consumers are turning to theInternet for services that help them manage more of their day-to-dayoffline activities and needs.

A search engine may attempt to determine the intent of a consumer whojust performed a search. In one example, the intent that is analyzed maybe geographic, such as for a search for the keyword “dry cleaning,” itis likely that the intent of the consumer is a local intent, such thatthe results should be directed to a specific geographic location. Localintent may refer to whether the consumer would like geographicallyspecific (local) results or whether there is a geographic component tothe search query. Accordingly, the search engine may determine thepresence of local intent of certain keywords and attempt to providesearch results that are targeted to a specific geography. Local intentmay refer to any aspect of intent that the consumer would like to see inthe results. The search engine determines and analyzes that intent toproduce search results that satisfy the intent of the consumer. Thelocal intent may be geo-related, rather than geo-specific, such thatsearch results for goods or services from a national online retailer maybe targeted to minimize shipping costs or other expenses.

The consumer who searches for a local dry cleaner should be showncontent, advertising and listings that are geographically relevantbecause it is likely the consumer is only concerned with dry cleanerslocated at a certain geographic location. In addition, the relevantadvertisements may relate to which local dry cleaners have specials,which don't use harmful chemicals, which are open late, and which arerated best by their community. General online information or websitesabout “dry cleaning” may not be relevant to a consumer concerned onlywith local dry cleaners. Directory information, social media, maps, andadvertising may all contribute to help the consumer make the bestdecision. Accordingly, geographic relevancy plays a greater role (incombination with contextual relevancy) in driving overall relevancy.

Some consumers may explicitly geo-modify their searches to specify alocation. For example, a consumer may search for “Chicago dry cleaning,”which shows local intent for dry cleaners in Chicago. However, there maybe hundreds of dry cleaners in Chicago, and the geographic location mayneed to be narrower than Chicago to achieve useful results. In addition,smaller, lesser-known towns or cities that are included in a search mayresult in search results that are not relevant to the specific locationthat is referenced. Further, many users fail to explicitly geo-modifysearches, so the search engine must determine whether a consumer wouldlike geo-specific results and how big a range of geography should becovered.

As the amount of local search traffic increases, there is a need for asearch engine to respond with more geographically relevant results.Search behavior with local intent may gradually increase over time, asconsumers receive more relevant local media and advertising in responseto local keyword searches. Search traffic may contain more local intent,as users increasingly rely on the Internet as a primary source ofinformation for their local purchasing decisions. Accordingly, a systemthat accurately determines when a consumer has local intent anddetermines the geographical scope of that intent in order to select themost relevant content and/or advertisements would be beneficial.

BRIEF DESCRIPTION OF THE DRAWINGS

The system and/or method may be better understood with reference to thefollowing drawings and description. Non-limiting and non-exhaustiveembodiments are described with reference to the following drawings. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating the principles of the invention. In thefigures, like referenced numerals designate corresponding partsthroughout the different views.

FIG. 1 provides a simplified view of one embodiment of an operatingenvironment;

FIG. 2 is a block diagram of an exemplary user system;

FIG. 3 is a block diagram of an exemplary network system;

FIG. 4 is a flow chart illustrating one embodiment for searching;

FIG. 5 is a flow chart illustrating one embodiment for determining localintent;

FIG. 6 is a block diagram illustrating location terms;

FIG. 7 is an illustration of a frequency distribution of a term;

FIG. 8 is a flowchart illustrating one embodiment for determining localintent probability;

FIG. 9 is an illustration of the relationship between locationdistribution and locality according to one embodiment;

FIG. 10 is a flowchart illustrating the determination of geographicrange according to one embodiment;

FIG. 11 is a diagram illustrating location bands;

FIG. 12 is a flowchart illustrating the use of geographic rangeaccording to one embodiment;

FIG. 13 is an illustration of location identification; and

FIG. 14 is an illustration a general computer system.

DETAILED DESCRIPTION

By way of introduction, the embodiments described below include a systemand method for determining local intent, determining a geographic rangeof the local intent, and providing relevant advertisements based on thatlocal intent and its geographic range. In particular, a search enginemay receive keywords or queries from users and the keywords are used todetermine local intent and select targeted content and/or advertisementsbased on the local intent and its geographic range. Local intent mayrefer to the intent that a consumer may have when performing a search,such as if the consumer desires geographic specific search results. Theembodiments relate to determining whether local intent exists,determining the geographic range of this local intent, and selectinggeographically and contextually relevant targeted content to bedisplayed.

Other systems, methods, features and advantages will be, or will become,apparent to one with skill in the art upon examination of the followingfigures and detailed description. It is intended that all suchadditional systems, methods, features and advantages be included withinthis description, be within the scope of the invention, and be protectedby the following claims and be defined by the following claims. Nothingin this section should be taken as a limitation on those claims. Furtheraspects and advantages are discussed below in conjunction with theembodiments.

FIG. 1 is a flowchart depicting an embodiment of using local intent insearching. In particular, FIG. 1 illustrates one embodiment utilizing asystem for incorporating local intent into a display of search resultsand/or advertisements in response to a user query. Embodiments of such asystem are illustrated in FIG. 2 and FIG. 3. A user may submit a searchquery to a search engine and receive a list of search results, such asvia a web page based display, menu, pop-up window, etc. The searchengine attempts to display the most relevant pages and information basedon the query entered by the user. Targeted content or advertisements mayinclude any features or information displayed on the search results pagein addition to the search results. The user query may include one ormore search terms or keywords related to a topic for which the user isrequesting pages or information.

In block 102, after a user submits a query to a search engine, thesystem recognizes which queries have local intent. Local intent refersto when a user submits a search query for which the user would likeresults that are local or geographically specific. For example, a searchfor “restaurants” likely has local intent in that the user would likesearch results that are for a certain location. In block 104, the systemmay determine the geographic range of the local intent. For example, therange may be the size of a geographic radius of the local restaurantsthat are displayed. The range may vary from country, state, city,neighborhood or any other measured distance. As described below, thegeographic range may be reflected in a local intent value (LIV). Inblock 106, the search query generates search results that aregeographically and contextually relevant based on the local intent andthe determined geographic range. As described in more detail below, asystem may be operative to recognize local intent, calculate thegeographic range of local intent and respond with geographically andcontextually relevant information.

FIG. 2 provides a simplified view of one embodiment of a networkenvironment 200. Network environment 200 may be for selecting andproviding online advertisements. Not all of the depicted components maybe required, however, and some embodiments of the invention may includeadditional components not shown in the figure. Variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the claims as set forth herein. Additional,different or fewer components may be provided.

The network environment 200 includes an advertisement services server210, which may provide a platform for selection, optimization, and/ordistribution of advertisements for inclusion in pages. Pages may beprovided to users by a portal server 204 and/or a third-party server202. In FIG. 2, users (also referred to as consumers) may be representedby the device by which they access the services of network environment200, such as with a user device 206, depicted as a conventional personalcomputer, and/or other device such as a mobile user device, including anetwork-enabled mobile phone, personal digital assistant (PDA), pager,network-enabled television, digital video recorder, such as TIVO®,and/or automobile. The user device 206 may be the user device asdescribed below with respect to FIG. 14.

Some or all of advertisement services server 210, portal server 204,and/or third-party server 202 may be in communication with each other byway of network 208 and may include the system or components describedbelow with respect to FIG. 14. The advertisement services server 210 andportal server 204 may each represent multiple linked computing devices,and multiple third-party servers, such as third-party server 202, may beincluded in the network environment 200. The network 208 may be regardedas a public or private network connection, such as a virtual privatenetwork or an encryption or other security mechanism employed over thepublic Internet, or the like.

The user device 206 may be represented by user-interactive devices thattypically run browser applications, and the like, to display requestedpages received over a network. The user may be a consumer of goods ofservices that is searching for a business such as a business that isassociated with advertiser. The device may communicate with the portalserver 204 and/or the third-party server 202 by way of the network 209.The network 209 may include the Internet or be the same as the network208. The networks 208 and/or 209 may be the network discussed below withrespect to FIG. 14.

The portal server 204, the third-party server 202, the advertisementservices server 210, and the user device 206 represent computing devicesof various kinds. Such computing devices may generally include anydevice that is configured to perform computation and that is capable ofsending and receiving data communications by way of one or more wiredand/or wireless communication interfaces. Such devices may be configuredto communicate in accordance with any of a variety of network protocols,including but not limited to protocols within the Transmission ControlProtocol/Internet Protocol (TCP/IP) protocol suite. For example, userdevice 206 may be configured to execute a browser application thatemploys HTTP to request information, such as a web page, from a webserver, which may be a process executing on portal server 204 orthird-party server 202.

The networks 208, 209 may be configured to couple one computing devicewith another computing device to enable communication of data betweenthe devices. Herein, the phrase “coupled with” is defined to meandirectly connected to or indirectly connected through one or moreintermediate components. Such intermediate components may include bothhardware and software based components. The networks 208, 209 maygenerally be enabled to employ any form of machine-readable media forcommunicating information from one device to another. Each of thenetworks 208, 209 may include one or more of a wireless network, a wirednetwork, a local area network (LAN), a wide area network (WAN), a directconnection such as through a Universal Serial Bus (USB) port, and thelike, and may include the set of interconnected networks that make upthe Internet. The networks 208, 209 may include any communication methodby which information may travel between computing devices.

The advertisement services server 210 may be used for providingadvertisements that are displayed to the user device 206. In particular,the portal server 202, or the third-party server 204 may be a searchengine that receives a search query from the user device 206 andresponds with search results. The portal server 202, or the third-partyserver 204 combined with the advertisement services server 210 may beutilized to determine local intent, determine the geographic range oflocal intent, and/or select the most relevant targeted advertisementsbased on the local intent described with respect to FIG. 1 and furtherdiscussed below with respect to FIGS. 4-13. In one embodiment, theportal server 202 may receive a search request and determine localintent, the third-party server 204 may determine the range of the localintent and the advertisement services server 210 may selectgeographically relevant advertisements to be displayed to the userdevice 206.

FIG. 3 is a block diagram of an exemplary network system 300. System 300may represent a more detailed and/or alternate embodiment of networkenvironment 200 as shown in FIG. 2. System 300 includes a user device302, an advertisement provider 306, and a search engine 304 coupled witha network 308. The advertisement provider 306 is coupled with anadvertisement filter 330 and an advertiser database. The search engine304 is coupled with a local intent analyzer 310 that receives data froman internal data source 320 and an external data source 322. The searchengine 304 further receives data from a user query database 326 and ageographic database 328. The local intent analyzer 310 and theadvertisement provider 306 are coupled with a matching engine 324.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional, different or fewer components may be provided.

The user device 302 may be any device that a user utilizes to connectwith the network 308. In one embodiment, the network 308 is the Internetand the user device 302 connects with a website provided by a webserver, such as search engine 304, coupled with the network 308. Inalternate embodiments, there may be multiple user devices 302representing the users that are connected with the network 308. A usermay not only include any individual, but a business entity or group ofpeople. Any user may utilize a user device 302, which may include aconventional personal computer, computing device, or a mobile userdevice, including a network-enabled mobile phone, VoIP phone, cellularphone, personal digital assistant (PDA), pager, network-enabledtelevision, digital video recorder, such as TIVO®, and/or automobile. Auser device 302 configured to connect with the network 308, may be thegeneral computer system or any of the components as described withrespect to FIG. 14. In one embodiment, the user device 302 may beconfigured to be coupled with the search engine 304 through the network308 with a web browser, such as INTERNET EXPLORER® or NETSCAPENAVIGATOR®. The web browser provides an interface through which the usermay perform a search. In alternate embodiments, there may be additionaluser devices 302, and additional intermediary networks (not shown) thatare established to connect the users or user devices.

The network 308 may generally be enabled to employ any form ofmachine-comprehensible media for communicating information from onedevice to another and may include any communication method by whichinformation may travel between devices. The network may be a network1426 as described with respect to FIG. 14. For example, the network 308may include one or more of a wireless network, a wired network, a localarea network (LAN), a wide area network (WAN), a direct connection suchas through a Universal Serial Bus (USB) port, and the like, and mayinclude the set of interconnected networks that make up the Internet.The wireless network may be a cellular telephone network, a networkoperating according to a standardized protocol such as IEEE 802.11,802.16, 802.20, published by the Institute of Electrical and ElectronicsEngineers, Inc., or WiMax network. Further, the network 308 may be apublic network, such as the Internet, a private network, such as anintranet, or combinations thereof, and may utilize a variety ofnetworking protocols now available or later developed including, but notlimited to TCP/IP based networking protocols. Any of the components innetwork 200 and/or system 300 may be coupled with one another throughother networks in addition to network 308.

The search engine 304 may be a content provider or a web server operatedover the network 308 that provides pages to users, such as to the userdevice 302. The search engine 304 may comprise a general computer systemor any of the components as described below with respect to FIG. 14. Inone embodiment, the search engine 304 is a webserver that provides awebsite that may be accessed by users and includes the ability toconduct a search over a network, such as the Internet. Yahoo!® is oneexample of a search engine embedded in a website (www.yahoo.com). Thesearch engine 304 may receive a search query from a user and providesearch results to that user. The search engine 304 may also provideother content and/or advertisements in addition to the search results.

The local intent analyzer 310 may determine and/or analyze local intentin search queries submitted to the search engine 304. The local intentanalyzer 310 may comprise a general computer system or any of thecomponents as described below with respect to FIG. 14. The local intentanalyzer 310 may be coupled with the search engine 304 or may be a partof the search engine 304. The local intent analyzer 310 is configured todetermine if a search query includes local intent and analyze the extentof that local intent, which may be used to generate the providedcontent, including search results and advertisements.

The local intent analyzer 310 may receive data from the internal datasource 320 and/or the external data source 322. In one embodiment, thedata sources 320, 322 may be one or more databases that are used tostore and/or provide information that may assist in the determinationand analysis of local intent within the local intent analyzer 310. Theinternal data source 320 may include search terms and traffic that areassociated with the search engine 304. The search engine's 304historical search data may be used to provide insight regarding localintent for searches by identifying which queries requiredlocation-specific information. The external data source 322 may includeany data from sources other than the search engine 304. For example,classification trees and topic taxonomies from pre-existing businessesthat express the intrinsic local nature of search terms such as thecategory headings of yellow page or direct mail companies, (e.g. “drycleaning” may be a popular, well-monetized advertiser heading).

The external data source 322 may include other sources, which use theUnited State's standard industrial classification code, (formerly knownas SIC code and now the NAIC). The industrial classification index maycontain topics and/or subtopics made up of inherently local searchterms, such as “Autos>AutoRepair>Bodyshops.” Accordingly, local intentmay be determined based on these identified topics, which may be presentin a search query. The percentage of time that a local term like “drycleaner” has commercial local intent may be determined from data sourcesof user behavior or commercial use of the term “dry cleaners.”Historical data of how the term “dry cleaners” is searched or howcommercial use of the term generates money in other businesses can formthe basis of the probability with which dry cleaners has local intent.The solution provides for the ability to use multiple data sources indetermining the probability of local intent for high volumes of userterms as described below.

The local intent analyzer may include a location extractor 312 which mayreceive a search query from the search engine 304 and determine if thesearch query includes a root term and/or a location term. The root termmay be the content or subject of the search, while the location termidentifies a location for that content or subject. A search query mayinclude a root term only that is the subject of the search, or it mayalso include a location term that identifies a relevant location for thedisplayed results that are related to the root term. For example, asearch query for “Chicago restaurants” has a location term “Chicago” anda root term “restaurants.” The location extractor 312 may analyze anddetermine if the query includes a location term, which may be separatedfrom the root term. The root term may include the remainder of the queryminus the location term. The analysis may be done for current searchqueries from the search engine 304 or may be from historical data fromthe internal or external data sources 320, 322. The extraction of a rootterm and a location term from a search query may be used in determiningand analyzing local intent for the search query.

The location extractor 312 may be coupled with the user query database326. The user query database 326 may include storage of root terms alongwith a measure of an associated local intent or possible location termsthat are associated with those root terms. The stored root terms mayinclude historical data related to search queries that include that rootterm. For example, if a root term is frequently coupled with a locationterm, then that root term is likely to have local intent. Accordingly,the user query database 326 receives data regarding the search queriesfor particular root terms. That data may used in determining localintent, which also may be stored in the user query database 326. In oneembodiment, the user query database 326 includes a list of root termsfrom historical search queries along with all the location terms thathave been searched with each of the root terms. For example, the rootterm “restaurants” may have been searched with the location terms“Chicago,” or “New York City.” Both of the location terms may berecorded in the user query database 326 as well as a frequency withwhich the location term has appeared with each root term in thehistorical search data.

The local intent analyzer may include an intent determiner 314 which maydetermine if a search query includes local intent. In one embodiment,the data from the user query database 326 may be used to determine aprobability of local intent for particular root terms. Accordingly, asearch query including certain root terms may be determined to havelocal intent. For example, a root term that is frequently associatedwith a location term may be an indicator that a search query with thatroot term has local intent.

The local intent analyzer may include a localness calculator 316, whichmay determine the geographic scope of the local intent for a searchquery. The localness calculator 316 may analyze the search query andcalculate the geographic scope of local intent as described below withrespect to FIG. 10. For example, the local intent of a search query for“dry cleaning” has a smaller geographic range than a search for “usedcar dealers.” The reason for the different geographic scope is thatconsumers may be willing to travel greater distances for a used cardealer than for a dry cleaner. Accordingly, a search for dry cleanershould have results, content and advertisements within a smallergeographic range. In one embodiment, the location term of a search querymay explicitly identify a geographic range of the search. For example, asearch for “dry cleaner in 1 mile” identifies that the geographic rangeis established to be 1 mile. Accordingly, the localness calculator 316may identify an explicit geographic range in a search query.

The local intent analyzer may include a location tagger 318, which maybe used to identify and label the locality of a search query. Thelocation tagger 318 may perform location tagging of a search query. Thelocation tagger 318 may be coupled with a geographic database 328 thatincludes geographic identifiers for locations. The geographic database328 may also be referred to as a geo-planet database. The geographicidentifiers may also be referred to as location identifiers. In oneembodiment, the geographic identifiers may include numbers, referred toas location or geographic identifiers, that identify its location asdescribed below with respect to FIG. 13. The geographic identifiersstored in the geographic database 328 may be used for the locationtagger 318 and provide a unique identifier for each geography, includingstates, cities, counties, zip codes, neighborhoods and other points ofinterest. In particular, each search query or root term may be taggedwith a geographic identifier by the location tagger 318.

A matching engine 324 may receive the analyzed search query from thelocal intent analyzer 310 and select an appropriate advertisement to bedisplayed with the search results. The matching engine 324 is coupledwith the advertisement provider 306. The advertisement provider 306receives a search query from the search engine 304 and/or receivesinformation on the search query from the matching engine 324, which isused for the selection of relevant advertisements to be displayed on theuser device 302. The matching engine 324 may be a part of theadvertisement provider 306 and/or the search engine 304 for selecting anadvertisement based on a search query.

The advertisement provider 306 may be a server that providesadvertisements to the search engine 304 and/or the user device 302directly. The advertisement provider 306 may comprise a general computersystem or any of the components as described below with respect to FIG.14. In one embodiment, the advertisement provider 306 may be associatedwith the search engine 304, such that the search engine 304 may includethe advertisement provider 306. In an alternate embodiment, theadvertisement provider 306 may be a content provider that also providestargeted content to be displayed with search results.

The advertiser database 336 may be coupled with the advertisementprovider 306. The advertiser database 336 may include availableadvertisements that are available to the advertiser provider 306. Theadvertiser database 336 may also include available advertisers as wellas information on size, content, pricing, and location specificity ofthe advertisements. In one embodiment, the advertiser database 336 mayinclude at least one location identifier for each advertisement, thatidentifies a geographic association of the advertisement. For example,advertisements may be specific to states, regions, cities, orneighborhoods, such as advertisements for a sports team in the cityand/or state that the team is located. Accordingly, an appropriateadvertisement may be matched with a search query based on the locationtagger 318. The location identifier(s) associated with eachadvertisement may be used for geographically matching theadvertisements. In addition, each advertisement may also be associatedwith a geographic range that establishes the geographic areas in whichthe advertisement is targeted towards.

The advertisement provider 306 may be coupled with or may include anadvertisement filter 330. The advertisement filter 330 may categorize ortag the available advertisements based on content and/or location. Forexample, the advertisement filter 330 may include root term associatedadvertisements 332 which categorize advertisements based on the rootterm of search queries. Root term associated advertisements 332 may beidentified and categorized based in part from the user query database326. Also, geo-identified advertisements 334 may categorize theadvertisements based on the location of the advertisement.Geo-identified advertisements 334 may be tagged according to the samegeographic identifiers as in the location tagger 318 with the geographicdatabase 328. Accordingly, the advertisement filter 330 may categorizeand filter available advertisements for appropriate matching of theadvertisements with a search query by the advertisement provider 306and/or the matching engine.

FIG. 4 is a flowchart illustrating one embodiment for searching. Inparticular, FIG. 4 illustrates the providing or display of relevantmedia and advertising in response to a search query that may be based atleast in part on local intent. In one embodiment, system 300, shown inFIG. 3, may perform the process illustrated by the blocks shown in FIG.4. In block 402, the user performs a search query. Accordingly, userdevice 302 may include a browser through which a user submits a searchquery over the network 308 to the search engine 304. The search engine304 may return search results to the user device 302 based on the searchquery in addition to targeted content and/or advertisements. The searchengine 304 may provide a way to find online information. One example ofa search engine 304 is Yahoo!® available or accessible at www.yahoo.com.The search query may also be referred to as a keyword(s), term(s),and/or search term(s). The search engine 304 provides search resultsthat are relevant based on the query.

Upon receiving the search query, the search engine 304 determines if thesearch query includes local intent (block 404). In block 404, if thereis no local intent, then in block 406, the search query is processed. Inone embodiment, the search query is received by the search engine 304and analyzed. The analysis may include extraction of a root term fromthe search query. In block 408, the search engine 304 may look for atext match to with the root term. Based on the text matches, in block410, relevant media and advertising is found. Because no local intentwas found in block 404, the search results as well as the advertisementsare based on the text of the search query and contextual matches withthe query. In block 412, the matched media and advertising may be rankedbased on relevance. The ranking may be based on the matching of thesearch query. Based on the rankings from block 412, the most relevantmedia and advertising may be provided in block 420. Accordingly, thesearch engine 304 provides the user device 302 with relevant searchresults and advertisements based on the provided search query.

Referring back to block 404, the determination of local intent mayinclude either an explicit local intent or an implicit local intent. Anexplicit local intent would be a location that is included in the searchas one of the keywords, such as “Chicago dry cleaning.” In particular,explicit local intent found in a search query that includes a locationterm in addition to a root term. The location term in the search queryshows explicit local intent. Implicit local intent may be certainkeywords that are known to frequently be associated with a particulargeographic location. For example, in a search for “dry cleaning” it isimplicit that the consumer would like geographic specific results.

Explicit local intent may be analyzed based on recognition of theexplicit location term. When an explicit location is recognized, thequery is parsed into its location term and root term, and the root termis said to have local intent. If the search is “Pasadena dentist,” thenthe local intent is determined to be the geographic location of Pasadenaand the root term that is contextually matched is dentist. In alternateembodiments, the location term may be an area, neighborhood, landmark,store or zip code. Colloquial areas, national parks, schools, hospitals,museums, sports stadiums, airports, public transportation locations,subway stops, and other types of popular landmarks may be otherlocations that consumers may identify.

Explicit location recognition parses location terms that may not be usedas a location. For example, a search for “Jack London” or “Paris Hilton”include a common location term, but in fact that location term isactually part of a name instead. Accordingly, the system may recognizesuch false positives. Further, ambiguous locations may also introduceerrors in recognizing local intent from explicit location names. Forexample, there exists a city named “Auto” in West Virginia, a city“Mobile” in Alabama, a city “Car” in France, and a city “George” inWashington State. Accordingly, the system may recognize ambiguouslocation names as well as false positives in which there is no localintent despite the presence of an explicit location term.

An implicit local intent may be found despite the absence of an explicitlocation term in the query. In one embodiment, the search engine 304 mayinclude or be coupled with a term database. The term database may be theuser query database 326, or may be an alternate database that storesroot terms, or keywords, as well as an indication to whether those rootterms are likely to include local intent. For example, searches for rootterms “restaurants” and “hotels” are likely to include local intent andmay be so labeled. Accordingly, a root term database may be utilized tohelp determine whether a local intent is present.

Both implicit local intent and explicit local intent may be identifiedand analyzed further as described below with respect to FIG. 5. Inparticular, FIG. 5 is a flow chart illustrating one embodiment fordetermining local intent. FIG. 5 illustrates an embodiment fordetermining local intent regardless of whether the local intent isimplicit or explicit. As illustrated, a probabilistic approach fordetermining local intent from multiple sources of data may not require adistinction between explicit and implicit local intent. The root termfor each search query may be associated with a local intent probabilitythat represents the probability that the root term includes localintent.

In one embodiment, there may be different probabilities calculated fordifferent sources of search query data. In block 502, sources of querydata are identified. As described above in system 300 for FIG. 3,internal data sources 320 and/or external data sources 322 may beutilized for search query data. In one embodiment, the various datasources used to provide search query data may be weighted in developinga probability of local intent based on the data sources. The datasources may be used in a probabilistic model to compute a local intentprobability based on the frequencies with which explicitly local searchqueries include locations. However, in order to control the strength ofeach contribution from multiple data sources, tunable parameters may beincorporated into the probabilistic model. The tunable parameters mayprovide the ability to weight the contribution from each data source andto control how both query traffic with locations and traffic withoutlocations can affect the local intent probability.

The presence of an explicit location is frequently indicative of localintent. Accordingly, the sources of search query data, such as internaldata sources 320 and/or external data sources 322, may be used todetermine which search queries and root terms were associated with anexplicit location. A root term that is frequently searched with alocation term has a high probability of local intent, while a searchquery that rarely includes a location term may have a low probability oflocal intent. Accordingly, the search query data may be used to developthe probability that a search query has local intent.

In block 504, large volumes of query data containing users' searchbehaviors may be captured and assembled for the purpose of determininglocal intent. This user query data may be captured by sampling largevolumes of statistically-representative user search queries frommultiple user query data sources. Query data sources may includedifferent types of searches, such as a general search over the web, ageographically specific search, a shopping related search, or any otherspecialized search. For example, some search engines may have a mainlinesearch as well as local search queries and/or queries conducted withinlocal online categories such as auto, shopping and real estate. Variousquery data sources may then be assembled using weighting functions toweight the contribution that each query source has in determining thelocal intent of a the search terms. The weights may be relative and mayindicate which query source best represents the local intent of a user.Each of the search query data sources are analyzed to extract arepresentative set of search queries that may be used in determininglocal intent.

In block 506, the root term is separated from the location term for eachsearch query from the data sources. As described in block 504, a set ofsearch queries may be extracted from each of the data sources and it isthose queries that are analyzed in block 506 by first determining theroot term and location term. In particular, the location extractor 312may remove the location term and leave the root term. In block 508, theset of queries are analyzed to identify location probability for each ofthe root terms removed in block 506. The analysis in block 508 may occursimultaneously with the removal from block 506.

In one embodiment, a probability of local intent is determined for aremoved root term. That root term may also be associated with a locationidentifier as described below. In block 510, the location probabilityfor each root term is averaged across multiple data sources for eachavailable query. The available queries may include a set of searchqueries as extracted in block 504.

In block 512, the search query data is analyzed and the queries arecategorized into location bands, which may be referred to as a locationtype. FIG. 6 is a block diagram illustrating location terms. Inparticular, the location term geographic range 602 may be broken downinto five bands. In alternate embodiments, there may be more or fewerbands that may or may not overlap. As shown in FIG. 6, the first andbroadest location band 604 is country. The country refers to thegeographic range of a country, such as the United States. The secondlocation band 606 is a state. State refers to any of the States in theUnited States. The state band 606 is encompassed by the country band 604and may be referred to as a child location to the country location band604. The third band 608 is a large city. A large city may cover thegeographic range of a large city such as Chicago or San Francisco. Thelarge city band 608 is encompassed by the state band 606 and may bereferred to as a child location of the state band 606. The fourth band610 is a small city. The small city or county band 610 may beencompassed by the large city band 608 and may be referred to as a childlocation to the large city band 608. In an alternate embodiment, thethird location band 608 or the fourth location band 610 may also includea county. A county may encompass a large city, or a county may encompassa large city, so the categorization of a county may vary.

The fifth location band 612 is a ZIP code, point of interest, orneighborhood. A neighborhood may be a subset of a city, such as LincolnPark is may be considered a neighborhood in Chicago. Points of interestmay include specific locations, such as landmarks, colloquial areas,parks, schools, hospitals, museums, sports stadiums, airports, publictransportation locations, subway stops, and other types of popularlandmarks. For example, a search query for “Wrigley Field bars” is basedon the location band 612 for a specific point of interest. The useridentifies a specific location, Wrigley Field, and would like to knowthe bars that are located around that location. It would not be relevantto provide bars throughout a city or county as in location band 610because a point of interest is identified establishing a narrowgeographic range.

As shown in FIG. 6, location bands may establish a geographic range. Asillustrated each successive location band covers a small geographicrange. The location bands covering a larger geographic range may bereferred to as a parent location to any smaller location bands withinthe larger geographic range. In alternate embodiments, there may be moreor fewer location bands and the location bands may or may not overlapwith one another. The identification of a location band may allow formore accurate targeting. For example, certain search queries may only berelevant for a State, such as location band 606, while other queries aremore narrow and relevant to a specific neighborhood, such as locationband 612. For example, a search query for “dry cleaners” in the statelocation band 606 would not yield relevant results, because thegeographic range is too large. Conversely, a search for “national parks”may be more relevant in the State location band 606 that isgeographically broad enough to cover potential matches.

Referring back to FIG. 5, in block 512, the search queries arecategorized into location bands, based on the location term geographicrange 602. Specifically, the location terms that are extracted from eachsearch query are categorized into a location band. The identifiedlocation bands may then be used in displaying search results andselecting advertisements.

In block 514, the search queries from the data sources are analyzedrelative to the location bands from block 512 to determine the frequencyeach query appears in a particular location band in the historicalsearch query data. For example, each root term of a search query mayappear with a variety of location terms. The frequency that the rootterm is associated with each location band is determined based on thehistorical search queries and is stored in a database, such as the userquery database 326. In other words, count-statistics are computed foreach root term to tally how many times it appears within each locationband based on historical search queries that matched the root term withlocation terms from each of the location bands. For example, the numberof queries containing the term “handyman” occur with no location, withany state name, with any large city name, with any small city name orwith any neighborhood, zip code, village, town or landmark name aretallied. It is likely that the frequency that the root term “handyman”is associated with more narrow location bands is higher than thefrequency that it is associated with broad location bands. Accordingly,the frequency in the neighborhood location band is likely greater thanthe frequency in the country location band because it is unlikely asearch query would want any handyman in the United States, but ratherwould identify a more specific location for finding a handyman. Thisfrequency analysis determines which location band that the root term“handyman” may belong.

FIG. 7 is an illustration of a frequency distribution of a root term.Specifically, the chart 700 in FIG. 7 illustrates the frequency withwhich a root term may be associated with different location bands orlocation types. Chart 700 is an illustration of the frequency oflocation bands for any root term by displaying the percent of explicitlocations along the y-axis, which is the percent that a location term ispresent in a particular location type. The x-axis illustrates fivelocation bands and the bar is the percentage of time each location term(associated with that location band) is found in a search query with theroot term at issue. In alternate embodiments, there may be additional oralternative location types.

For example, if the root term is “delivery pizza” then 29.8% of thesearches are for a large city, such as “Los Angeles delivery pizza.”However, 44.4% of the searches are for a neighborhood or point ofinterest, such as “Lincoln Park delivery pizza.” The explicit locationterms are analyzed and categorized into one of the location bands foreach root term. A search without a location term would suggest no localintent and would not be included in one of the location bands, or wouldbe a location band identified as not being associated with a particularlocation. Based on the frequency distribution shown in chart 700, theroot term of “delivery pizza” would be classified in the small citieslocation band or the neighborhood band.

Alternatively, for the root term “job,” chart 700 illustrates that thelocalness is lower than for “delivery pizza.” People are more likely toassociate a job with a large city or even a small city. Rarely would asearch for “job” focus on a neighborhood or a point of interest. Basedon the frequency distribution shown in chart 700, the root term of “job”would be classified in the large cities location band, or location band608 in FIG. 6.

FIG. 9 is an illustration of the relationship between locationdistribution and locality according to one embodiment. Chart 900 may beone embodiment of a distribution of location distribution as a functionof locality or LIV. Chart 900 may be an alternate illustration of thehistogram 700 shown in FIG. 7. Chart 900 illustrates that the functionmay be continuous rather than discrete across the locality values. Theremay be a grouping of location types (such as the five groups in FIG. 7),or the location types may be continuous rather than discrete as in Chart900.

Referring back to FIG. 5, in block 516, a probability of local intent orlocal intent probability (LIP) is calculated. The LIP may be calculatedin different ways, such as a statistical function of location bands, asa function of the LIV, or as a function of the LRP. Other examples ofLIP calculation may exist. The chosen calculation may be the one thatproduces the greatest commercial value. As new functions are discovered,that create more commercial value, they will be applied as well.

FIG. 8 illustrates three examples for the calculation of the LIP. Afirst LIP calculation (block 804) is a function of LIV. A second LIPcalculation (block 806) is a function of LRP. A third LIP calculation(block 808) is a statistical function of location bands. These threeexemplary LIP calculations will be discussed below. The LIP calculationmay be modified to attempt to accurately determine which searches havelocal intent. As more data is available, the model used for calculatingLIP may evolve and may be determined based on commercial value. As newfunctions are discovered that create more commercial value, they may beapplied as well. The LIP may be based on the count and frequencydistribution combined with an appropriate statistical model. Thestatistical formulas may evolve as more data sources become available.FIG. 8 is a flowchart illustrating one embodiment of determining localintent probability (LIP). Specifically, FIG. 8 illustrates examples ofcalculations of the LIP value.

In block 802, the LIP is calculated with a statistical probabilitymodel. In alternate embodiments, there are alternative models that maybe used to calculate the LIP than the three that are illustrated. In oneembodiment a local intent value (LIV) may be used in the statisticalfunction. In a second embodiment, the LRP may be used as the statisticalfunction. In a third embodiment, the location band distributions foreach term may be used as the statistical function. The LIV may arepresentation of the geographic range of the local intent and isfurther described below in block 1012 of FIG. 10. The LIP may be afunction of LIV(term) as in the formulas shown in FIG. 8.

In block 804, the LIP is a function of a local intent value (LIV)combined with an error term. In particular, LIP[term]=f(LIV)+error,where term is the root term from the search query for which the LIP isbeing calculated. Block 805 is one example of this LIP calculation andmay be an LIP regression model. Specifically, the LIP may be calculatedto be:

${{{LIP}\mspace{11mu}({term})} = {\frac{\exp^{\lambda + {\theta\;{LIV}\;{({term})}}}}{1 + \exp^{\lambda + {\theta\;{LIV}\;{({term})}}}} + ɛ}},$where the LIV is calculated as described above. Error (ε) model can bewhite noise process i.e. normal with mean=0, standard deviation=σ or anystationary process whose parameters can be calculated empirically. In amathematical notation, ε=N(0,σ). LIP(term) is the probability of a termbeing local intent or not and λ (lambda) and θ (theta) are theparameters of the model. The value of λ yields LIP when LIV(term) iszero, and θ adjusts how quickly the probability changes with changingLIV(term) a single unit

In block 806, an alternative statistical probability model is used forcalculating the LIP based on a location related probability (LRP)combined with an error term. The LRP may be a quantification ofprobability that a search term is related to geography, or a specificlocation. In particular, in block 806, LIP[term]=f(LRP)+error.

An alternative statistical probability model for calculating the LIP isshown in block 808. In particular, LIP[term]=g(freq_(location band l), .. . , freq_(location band k), location query frequency, relativefrequency of verticals, . . . )+error. In an abbreviated expression, asshown in block 808, the LIP(term) may be f(term, location band)+error.The location query frequency may be calculated from the distributionhistogram of location bands for each term. This direct LIP formula maybe based on weighted data from the location bands.

Alternatively, the location query frequency may be calculated as thenumber of root term queries in a particular location band divided by thetotal number of root term queries. The relative frequency of a term fora query source data may be the percentage of that term occurring out ofall or a subset of search for a specified time interval. In analternative embodiment, there may be a different LIP regression model.

An LIP regression model is shown in block 809. Specifically, the LIP maybe calculated to be:

${{{LIP}\mspace{11mu}({term})} = {\frac{\exp^{\lambda + {\sum{\theta_{i}f\mspace{11mu}{({{term},\;{LocationBand}_{i}})}}}}}{1 + \exp^{\lambda + {\sum{\theta_{i}f\mspace{11mu}{({{term},\;{LocationBand}_{i}})}}}}} + \omega}},$where the location bands are any of the location types as discussed inFIG. 6. The variable i represents the number of location bands, which isfive in the example shown in FIG. 6. Exp( ) is an exponential functionand ω and δ each represent an error model. Error model (ε) may be normalprobability distribution with mean=0 and standard deviation=σ fromempirical data, or in mathematical notation ε=N(0,σ) as one of thealternatives. In one embodiment, the LIV calculation may be based onfrequencies for each location band. Therefore, the local intentprobability model may be represented theoretically in terms of frequencyi and other factors such as location query frequency. λ represents whenfunction f(term, location Band_(i))=0 and θ_{i} represents how quicklythe probability changes when f(term, location Bands) a single unit.Because the relation between f( ) and LIP( ) may be nonlinear, θ_{i} maynot have a straightforward interpretation in this model as it does inordinary linear regression. Accordingly, this is an example of a classof statistical model (nonlinear) multivariate logistic regression. Thefunction f in the formula may represent the frequency of the term in aparticular location band.

Referring back to FIG. 5, after the LIP is calculated in block 516, amanually identified truth set is developed for error analysis in block518. An editorial truth set may be established in which editors maymanually identify the local intent of a large sample of terms. Theeditors manually determined local intent values may be used to for errorcorrection, for tuning or parameters, or as a training set. Using one ofthe tunable probability models with error functions as discussed abovemay allow for the tuning of model parameters to replicate or exceed theprecision and recall of the editorially-controlled truth set. The truthset may be used to minimize the error function. The truth set may be aset of root terms provided for determining to what extent computed LIPvalues based on the proposed system and method match with measurededitorial local intent values. The truth set may provide a training setwhich is used to calibrate the parameters used in calculating the LIPvalues and further used to estimate a representative error model. Inblock 519, the LIP function is tuned with the truth set to minimizeerrors. In other words, truth sets are used to “tune” the functions forLIP and for LIV, to minimize error. Once the functions are tuned, theymay be re-run to output another list of terms with their LIP and LIVparameters for each term.

In block 520, a list of the root terms and their corresponding LIP maybe outputted. In one embodiment, the search engine 304 and/or the localintent analyzer produce the list of terms with their associated LIPvalue. In particular, the list of root terms and their local intentprobability are stored in a table for use within applications that scanstructure or unstructured text and queries to identify the local intentof the search terms or text terms scanned. In one embodiment, the listof root terms and LIP values may be stored in the user query database326.

Accordingly, FIG. 5 illustrates one embodiment for determining thepresence of local intent. The presence of local intent was determined inblock 404 of FIG. 4 and in block 102 of FIG. 1. In particular, thedetermination of local intent is relevant because search queries thathave local intent may receive search results in which geography isweighted more heavily as discussed below. In addition, local intent maybe used for providing geographically targeted content and advertisementswith the search results. Conversely, search queries that have less or nolocal intent will receive search results in which geography is lessheavily weighted, or not weighted at all. Once local intent isestablished, the geographic range of the local intent may be determinedas shown in block 104 of FIG. 1.

Referring back to FIG. 4, if local intent is determined to be present inblock 404, then the local intent value (LIV) is calculated as in block414. Conversely, as discussed above, if local intent is not found inblock 404, then the search query is processed without consideringgeography. In one embodiment, if the LIP is determined to be above acertain threshold, such as 50% or 75% probability of local intent, thenthat may be a determination that local intent is present in block 404.

The local intent value (LIV) is calculated in block 414. In particular,the LIV is a representation of the geographic range of the local intent.The geographic range of local intent is used as a measure of aconsumer's consideration of a commercial offer or media content. Thegeographic range for a user to consider offers or media content may varybased on the product or service being purchased, or the media beingconsumed.

In one example, a person buying a car may have a large range for theirwillingness to purchase the car they want, of 5-50 miles or more fromtheir home or work location. This range of 5-50 miles may be consistentwith the general size of a large city metro area or a county.Conversely, a person who needs to dry clean their clothes will have amuch smaller range for a dry cleaner, such as a 1-5 mile radius from theuser's work or home. This range of 1-5 miles may be consistent with thegeneral size of a small city, town, neighborhood, or suburb.Accordingly, the geographic range of the purchase opportunity for theroot term “dry cleaners” is much lower than that for the root term“new/used cars.” In other words, the term “dry cleaner” has greaterlocalness in terms of commercial value than the term “used cars.” Theestablishment of this geographic range may then be used in providingsearch results, content and/or advertisements that are within thegeographic range.

The geographic range of searched root terms may be correlated to thefrequencies with which their root terms are searched with geographicmodifiers for explicit local intent. The range varies across the rootterms. For example, root terms such as “mover,” “home builder,” and “newcar dealer” may be associated with states and the larger cities 60-70%of the time. Conversely, root terms such as “dry cleaner,” “mall,”“condo rental,” “medical center,” and “winery” may be associated withsmaller cities, neighborhoods, and/or landmarks over 70% of the timeindicating that these root terms have a smaller geographic range.Accordingly, a user query for “malls,” “stores,” or “dry cleaners,” mayresult in geo-targeting of advertising offers and media content that isvery local with a small geographic radius. However when a user searchesfor “new auto dealer,” “mover,” or “home builder,” geo-targeting mayrespond with city, metro, or large geographic radius advertising offersand media content.

The local intent value (LIV) as calculated in block 414 is aquantification that identifies the range of local intent for highvolumes of root terms. The LIV may offer a measure of localness for eachroot term in a search query. The LIV quantifies queries with lowlocalness, (such as “new homebuilder, mover, lottery, tickets”), as wellas those with high localness, (such as “dry cleaner, mall, outlet,medical center and winery”). Accordingly, the LIV represents thegeographic range of a root term, with a higher LIV representing anarrower geographic range and a lower LIV representing a largergeographic range.

FIG. 10 is a flowchart illustrating the determination of geographicrange according to one embodiment. In particular, FIG. 10 calculates anLIV value as in block 414 of FIG. 4. As described below, the LIV of eachroot term may be represented as a continuous distribution function andapproximated by a discrete probability function in which the discretevalues are location bands. This discrete probability function may takemultiple data sources as input so that as the number of data sourcesincreases, the confidence of the probability of the LIV improves. Anexpected value for the LIV of a root term may then be determined foreach data source.

In block 1002, data sources containing search query data are located. Asdescribed above in block 502 of FIG. 5 and in system 300 for FIG. 3,internal data sources 320 and/or external data sources 322 may beutilized for search query data. The presence of an explicit location maysuggest a geographic range which may be used to calculate a LIV.Accordingly, the sources of search query data, such as internal datasources 320 and/or external data sources 322, may be used to determinewhich explicit locations are commonly associated with which root termsin search queries. A root term that is frequently associated with anarrow geographic range will likely have a higher localness value than aroot term that is frequently associated with a large geographic range.Accordingly, the search query data from multiple data sources may beused to develop the LIV for each root term.

In block 1004, large volumes of query data containing users' searchbehaviors may be captured and assembled for the purpose of determininglocal intent similar to block 504 of FIG. 5. This user query data may becaptured by sampling large volumes of statistically-representative usersearch queries from multiple user query data sources. Each of the searchquery data sources may be analyzed to extract a representative set ofsearch queries that may be used in determining local intent.

The queries stored or recorded in the data sources may be analyzed todetermine which queries include explicit locations. Queries withexplicit locations or explicit local intent may also be referred to asgeo-modified queries because they include a location term in addition toa root term. In block 1006, the root term and the location term areseparated for each query that includes a location term.

In block 1008, the location band of the explicit location term isdetermined and stored. In one embodiment the location bands that areavailable are shown in FIG. 6. The five location bands described withrespect to FIG. 6 may be used in the quantification of the LIV. Inalternate embodiments, more or fewer location bands may be used in thecalculation of the LIV. Explicit search queries having local intent maybe analyzed for how often a specific location band appears with the rootterm as discussed above. The output may be a list of root terms andtheir respective LIV from 0-5 corresponding to the five location bandsfrom FIG. 6. Terms with higher LIV values, (3-5) have high localness,and those with lower LIV values (1-2), less localness. A LIV value ofzero (“0”) may indicate zero local intent and that localness does notapply and the root term has no local intent.

FIG. 11 is an illustration of one embodiment of the location bands. Inparticular, FIG. 11 illustrates the five location bands that weredescribed with respect to FIG. 6. In one embodiment, the five locationbands may be used in the quantification of the LIV. In alternateembodiments, more or fewer location bands may be used in the calculationof the LIV. Explicit search queries having local intent may be analyzedfor how often a specific location band appears with the root term asdiscussed above. The output may be a list of root terms and theirrespective LIV from 0-5 corresponding to the five location bands shownin FIG. 11.

As shown in FIG. 11, the smallest location band is the fifth locationband 1102, which may represent a neighborhood. The fourth location band1104 may represent a small city and may encompass the smaller locationband 1102. The third location band 1106 may represent a large city andmay encompass the smaller location bands 1102, 1104. The second locationband 1108 may represent a regional area, such as a state, and mayencompass the smaller location bands 1102, 1104, and 1106. Finally, thefirst location band 1110 may represent a national area, such as acountry, and may encompass the other location bands.

The larger geographic areas, such as the first location band 1110 have alower LIV, whereas the smaller geographic areas, such as the fifthlocation band 1102 has a larger LIV. The localness (and the LIV)increases as the geographic area is narrowed. In one embodiment, for thefifth location band 1102 the LIV=5, for the fourth location band 1104the LIV=4, for the third location band 1106 the LIV=3, for the secondlocation band 1108 the LIV=2, and for the first location band 1110 theLIV=1.

Referring back to FIG. 10, the location band information from eachsearch query may be used in block 1010 to determine the frequency withwhich each root term is associated with each of the location bands. Forexample, the root term “dry cleaners” may be searched with an explicitlocation term associated with 1) no location or country (location band1); 2) a State location (location band 2); 3) large city or county(location band 3); 4) small city (location band 4); or 5) neighborhood,zip code, or smaller location (location band 5). The frequency that alocation term is searched with the root term is measured and recorded ineach location band that is associated with the location term.

Each search query with a location term is analyzed to determine thelocation band. The location bands are recorded along with the root termthat they are associated with. In other words, each search query with alocation term has a root term that is associated with a location bandthat corresponds to the location term. The frequency of each locationband for each root term may be recorded in a histogram. FIG. 7illustrates one example of a histogram that may reflect the percentageor frequency of each location band for a particular root term. In otherwords, FIG. 7 may show that for a particular root term, there is apercentage by which each location band may be associated with that rootterm.

In block 1012, the local intent value (LIV) may be calculated based onthe search query data of the location bands associated with each rootterm. In one embodiment, the LIV is calculated by:

LIV (term)=Σ frequency(term, location band i)×locality integer I; where“i” refers to the five location bands (1-5) that are shown and describedwith respect to FIG. 6. In addition, no location band may be referred toas location band 0 and the value i for no location may be zero.

As one example of the calculation of LIV the following frequency chartmay be used that illustrates a root term with the frequency for thatterm in each location band. This frequency chart corresponds with thevisual representation of the location bands shown in Chart 700 of FIG.7.

Root term Country State Large city Small city Neighborhood Delivery 0.0% 5.0% 29.8% 20.4% 44.4% Pizza Job 7.3% 19.1% 35.3% 33.2%  0.2%

According to the LIV formula listed above, the LIV may be calculatedaccording to the above formula: LIV(deliverypizza)=0%×1+5.0%×2+29.8%×3+20.4%×4+44.4%×5=4.03. The LIV for the rootterm delivery pizza is 4.03, which corresponds to a small city (locationband 4) suggesting that the root term “delivery pizza” is most oftensearched for in the location of a small city. Conversely, for the rootterm “job” the LIV is calculated as:LIV(job)=7.3%×1+19.1%×2+35.3%×3+33.2%×4+0.2%×5=2.852. The LIV of 2.852for the root term “job” is closest to location band three, or a largecity. Accordingly, the root term “job” has lower localness than the rootterm “delivery pizza.” The formula described above may provide for theability to weight how often a term is searched with any location name.

The formula may be expanded to include no location type, whichcorresponds to a location band of zero. Accordingly, the LIV formula maybe expanded to a probability distribution formula:LIV=ΣProbability(term, location band i)*Expected Value(term, locationband i)+Probability(term, no location)*Expected Value(term, no locationband). The probability distribution formula may be an extension to theLIV formula described above. Probability(term, no location) may be astatistical estimate of the percentage of occurrences of a term withoutlocation name. Expected value(term, no location band) may a defaultvalue for the local intent value (LIV) in the absence of any query withlocation names. Different source tags may be utilized to determine thedefault value, at least partly based on the relative frequency of theterm for source tags.

The way in which queries for a particular term are weighted and used inany of the LIV functions may differ. The weighting and functions thatare used may vary and may depend on the search data that is available.In addition, the formulas may be modified over time to more accuratelydetermine the local intent of users.

In one embodiment, a different LIV may be calculated for each of thedata sources that are available. Accordingly, in block 1014, the LIVfrom each data source may be combined and/or averaged to get an overallLIV score. The formula described above may be expanded to account formultiple data sources. Accordingly, the LIV formula may be expanded to aprobability distribution formula: LIV=ΣΣProbability(term, location bandi, source j)*Expected Value(term, location band i, sourcej)+Probability(term, no location)*Expected Value(term, no locationband). The variable “i” refers to each location band, and “j” refers tothe potential sources of frequency data. The summations are overvariables “i” and “j.” Alternatively, the formula may beLIV=f(probability term, location band i, source j)+error, where thevariable “i” refers to each location band, and “j” refers to each sourceof frequency data.

A manually identified truth set may be developed for error analysis inblock 1016. An editorial truth set may be established in which editorsmay manually identify the local intent of a large sample of terms. Usingone of the tunable probability models with error functions as discussedabove may allow for the tuning of model parameters to replicate orexceed the precision and recall of the editorially-controlled truth set.The truth set may minimize the error function. Accordingly, eachadditional data source that contributes to the calculation of the localintent probability of each term may be refined. In block 1018, the LIPfunction is tuned with the truth set to minimize errors.

In block 1020, the root terms and corresponding LIV's may be stored in atable or list. In one embodiment, the list is stored in the user querydatabase 326. The user query database 326 may include a list of all rootterms from the search query data sources as well as associated locationterms, associated location bands, frequency of the term in each locationband, LIP and LIV. Alternatively, each root term may be stored with onlythe LIV.

The LIV formulas may be adapted to include additional data andinformation to increase the predictive value for each LIV. For example,algorithms may account for demographics and time/day. In addition,algorithms may consider the context of the property in which the term isbeing searched, such as mobile versus local news versus national news.

As discussed below, queries that have high local intent values may havethe user location geo-modified at a smaller city or neighborhood level.Those queries may receive search results in which local/neighborhoodgeography may be weighted more heavily. Queries that have medium or lowlocal intent values may have the user location geo-modified with alarger city metro level. Those queries may receive search results inwhich geography is moderately weighted or combined with nationaladvertising. Queries that have no local intent will not have the userlocation geo-modified with any location type and will receive searchresults in which geography is not weighted at all, thus onlycontextually relevant or topic-driven advertising will be served.

Referring back to FIG. 1, after the geographic range is quantified inblock 104, geographically and contextually relevant information may beprovided in block 106. Specifically, the search engine 304 and/or theadvertisement provider 306 may select the appropriate search results,media, information, and/or advertisements to be displayed on the userdevice 302 in response to a search query. The advertisement provider 306would like to provide geographically targeted advertisements, which maybe more effective based on the recognition of local intent and anidentification and quantification of the geographic range of the localintent. In addition, it may not be possible to exactly match thegeographic range of a query, so it may be necessary to expand thegeographic range or select geographic neighbors when relevant. Forexample, the advertisements that may be shown for a search query on “LosAngeles dentists” are related to the query “Santa Monica dentists.”Accordingly, an advertisement or other content that is associated with“Santa Monica” may be displayed for a search query of “Los Angelesdentist,” if there advertisements or content specific to Los Angeles arenot available. It may be necessary to determine and select the mostgeographically relevant information and advertisements based on what isavailable.

Referring back to FIG. 4, the geographic range of LIV is calculated inblock 414 as described above with respect to FIG. 10. After the LIV iscalculated, geo-relevant media and advertising may be found in block416. In one embodiment, the LIV is used to determine which media andadvertisements are geographically relevant.

FIG. 12 is a flowchart illustrating the use of geographic rangeaccording to one embodiment. In particular, FIG. 12 illustratesproviding a response to a search query that includes providinggeographically relevant media and advertisements. In block 1202, a queryis received and analyzed for geography or local intent. As discussedabove in blocks 404 and 414 of FIG. 4, the local intent and the range ofthat local intent may be determined, such as in the intent determiner314 and localness calculator 316 of FIG. 3. That information may be usedto identify a location. That identified location may then become a partof the query in block 1202, such that the query may be referred to as ageo-modified query. A geo-modified query may include a location term orthe root term may have high local intent. The geo-modification of thequery may be the association of a location with the query.

In alternate embodiments, the identification of a location in block 1202may be determined differently. In one embodiment, U.S. PatentPublication No. 2005/0060430, published Mar. 17, 2005, entitled “METHODOF DETERMINING A LIKELY GEOGRAPHICAL LOCATION,” which is incorporated byreference herein, determines the likely geographic location based onsubmitted criteria. In addition, U.S. Patent Publication No.2005/0108213, published May 19, 2005, entitled “GEOGRAPHICAL LOCATIONEXTRACTION,” which is incorporated by reference herein, infers a likelygeographical location from one or more search terms. In one embodiment,the location may be determined based on the users IP address as in U.S.Patent Publication No. 2005/0108244, published May 19, 2005, entitled“METHOD OF DETERMINING GEOGRAPHICAL LOCATION FROM IP ADDRESSINFORMATION,” which is incorporated by reference herein. For a searchperformed on a mobile device, the geographic location may be determinedas in U.S. Patent Publication No. 2005/0003835, published Jan. 6, 2005,entitled “METHOD OF PROVIDING LOCATION BASED INFORMATION TO AMOBILE-TERMINAL WITHIN A COMMUNICATIONS NETWORK,” which is incorporatedby reference herein.

In block 1204, the identified location is removed from the root term ofthe query and is given a location identifier. The root term may beremoved from the location term by the location extractor 312 in FIG. 3,as described above. The location term is parsed and used for tagging thequery with a location. The location tagging may also be referred to as ageographic identifier, location identifier, or geographic tag. In oneembodiment, the location tagger 318 is in the local intent analyzer 310of FIG. 3, and may perform the location tagging using a geographicdatabase 328 that includes location identifiers for various locations.

The location identifier may be a unique identification for any location.In one embodiment, each city, town, county, zip code, and state in theUnited States is associated with a unique location identifier. Anexample of assigning location identifiers is described in U.S. PatentPublication No. 2006/0004797, published Jan. 5, 2006, entitled“GEOGRAPHICAL LOCATION INDEXING,” which is hereby incorporated byreference. That application describes zones that are represented bylocation identifiers that establish relationships and overlap withadjacent zones.

The location identifier may be used to record adjacencies betweenlocations. In addition the location identifier may also be used toidentify a parent location or child locations. In one embodiment, thelocation identifiers and the relationships between locations may becategorized as described with respect to FIG. 6 and FIG. 11. FIG. 13illustrates one embodiment of location identification. In particular,FIG. 13 illustrates five location bands that may be used to define therelationships between locations, such as adjacent locations, orparent/child locations. The country location 1302 is the broadestlocation in that it covers a country, such as the United States. It islabeled with location identifier 1099. Country location 1302 is a parentlocation for all the states and is a grandparent location to all otherlocations below the states. State location 1304 is a child location ofthe country location 1302. State location 1304 represents California,which is labeled with location identifier 345. Also shown is an adjacentstate location 1306 that represents Nevada. The location identifier of345 for California may be associated with all adjacent states, such asNevada, Arizona, and Utah as shown in state location 1304. The childlocation of the state location 1304 is the large city location 1308. Thecity of Los Angeles is identified by location identifier 245. Thelocation identifier of 245 is associated with its parent locations,California (345) and USA (1099). In addition, it may be associated withadjacent cities, such as Burbank, Santa Monica, San Gabriel, andGlendale. In particular, adjacent large city location 1310 Ventura isshown.

One example of a child location for a large city location 1308 is asmall city location 1312. As shown, San Gabriel is a smaller cityidentified by location identifier 37. Adjacent smaller cities that areassociated with location identifier 37 are Temple City, Alhambra, andSierra Madre. In addition, the adjacent small city location 1314 ofPasadena is shown. San Gabriel is a child location of Los Angeles. Childlocations of a small city location 1312 may include zip code locations1316. In an alternative embodiment, the child locations of a small citylocation 1312 may include specific points of interest or neighborhoodsas described above.

The chart 1320 in FIG. 13 illustrates the location identifiers of someof the locations in FIG. 13. In particular, the chart 1320 assumes thatthe exact location is the smaller city of San Gabriel, which haslocation identifier 37. As shown in the chart 1320, the parent city zoneis Los Angeles (identifier 245), the parent state is California(identifier 358), and the parent country is the United States(identifier 1099). Each of these parent locations may be referred to ashead locations because they overlap or subsume the exact location inquestion, which is San Gabriel. Conversely, tail locations are thosewhich are adjacent the exact location or are child locations. As shownin chart 1320, the adjacent or tail locations of San Gabriel includeSouth San Gabriel, South Pasadena, Alhambra, San Marino, Pasadena,Temple City, and Arcadia. Each of the adjacent cities are associatedwith a unique location identifier. Accordingly, the location identifier37 of San Gabriel may be associated with all of the head locations andthe tail locations. Accordingly, a database, such as the geographicdatabase 328 may include a listing of every location identifier as wellas the relationships between the location identifiers. For example, thehead locations and the tail locations for each location identifier maybe recorded in the geographic database 328. Accordingly, referencing anyof the location identifiers stored in the database will allow for easyaccess to all parent, child and adjacent locations.

Accordingly, in block 1204 of FIG. 12, the location term of a searchquery may be parsed and tagged with a location identifier. That locationidentifier may then be used in displaying search results, relevantcontent and advertisements that are targeted based on the location. Inblock 1206, the root term is analyzed for localness, which may be basedon a local intent value (LIV). As described above, in block 1202, alocation is identified for a query and/or for the user. Localness or LIVis a measure of the geographic range of that location. As discussedabove, certain queries such as “dry cleaning” have a higher localness orLIV than other queries, such as “used car dealers.”

The localness value may be used to determine which locations areincluded for the providing of search results, content and/oradvertisements as in block 1208. The location identifier is used toidentify an exact location as discussed with respect to FIG. 13. Thatexact location may be a country, state, large city, small city, zipcode, point of interest or other location. The exact location maydetermine or influence the localness. In block 1208, a determination ismade for any expansion or contraction of the geographic area that isrelevant based on the search query. An exact location of a state mayresult in an expansion to other states (adjacent locations) or acontraction to a selection of certain areas within that state, such asits child locations. Conversely, an exact location of a small town maybe expanded all the way out to the state level to include parentlocations. Accordingly, the localness of the search query combined withthe location identifier may be used to expand or contract the relevantgeographic area.

In one embodiment, if the localness or LIV is high, which suggests asmaller geographic range, then the relevant geographies may becontracted to find locations in that range. For example, if the LIV is asmall city and the location associated with an advertisement is at thestate level, then the small city location is expanded to its parentlocations to select relevant advertisements. Accordingly, the locationidentifier may be used for the expansion of geographies to coveradjacent locations, parent locations, or child locations. The expansiondown to child locations may be referred to as contraction.

In block 1210, location identifiers are used for an expansion of thegeographic area. In one embodiment, an expansion list of locationidentifiers is generated based on the exact location identifier. Asdiscussed above, the geographic database 328 may include relationshipsbetween parent, child, and/or adjacent locations. Accordingly, anexpansion may include all adjacent locations and/or the parent location.The expansion or contraction may be based on the available searchresults, content, and/or advertisements. For example, advertisements mayonly be available on the large city level. Accordingly, if the exactlocation is a small city, then expansion of that location may give alarge city location that is associated with an advertisement. In oneembodiment, the available advertisements may only be related to certainareas. For example, if the only advertisements that are associated withIllinois relate to Chicago, then if the exact location is Peoria, Ill.,expansion into the parent location of Illinois may result in Chicagoadvertisements being displayed. Likewise, the expansion of Peoria, Ill.into adjacent locations may also result in Chicago advertisements beingdisplayed.

In block 1212, geo-relevant advertisement candidate listings areretrieved from the advertiser database. In one embodiment, the listingsare stored in the advertiser database 336 discussed with respect to FIG.3. In alternate embodiments, the candidate listings may include searchresults or other content that is geo-relevant and/or targeted based onthe location, but as discussed below refers to candidates foradvertisements. In block 1214, the root term of the query is firstmatched by contextual relevancy when selecting targeted search resultsand/or advertisements. In block 1216, the contextually relevant resultsmay then be filtered based on the exact location or the locationidentifier associated with the query and/or the location term. Thefiltering may be accomplished by the advertisement filter 330 in FIG. 3.As a result, a set of potentially relevant advertisements may beestablished based on contextual and/or geographic relevance. The set ofcandidates may then be filtered for selection of at least one relevantadvertisement to be displayed with the search results.

In block 1218, the candidate set of geo-relevant listings may be ranked.The candidate set may be relevant advertisements or may be searchresults or other content. In one embodiment, a quality scoring functionmay rank the candidate set as in block 1220. The quality scoringfunction may weigh an advertisements relative bid for the advertisement,as well as a willingness to pay to the marketplace, click-thru-rate, andoverall advertisement relevancy.

The quality scoring function may be used for scoring which relevantadvertisements may appear first in response to a user query having localintent. Any quality scoring function may be used to rank theadvertisements and to improve geo-relevancy of the relevantadvertisements. In one example, the rank of an advertiser may determinedbased on the advertisement's eCPM value which may be defined asf(quality score)×g(bid) value. eCPM may represent effective clicks perthousand, which determines when a user clicks on an advertisement. Thequality score may be a linear function of machine learning searchalgorithmic relevancy, which may include past click through data. Thequality score may further relate to local intent and advertisergeo-relevancy factors, such that ads are ranked and served once eCPMvalues are calculated for each query. In one embodiment, advertisementservices are described in U.S. Patent Publication No. 2006/0282314 A1,published Dec. 14, 2006, entitled “UNIVERSAL ADVERTISEMENT SERVICESARCHITECTURE,” which is incorporated by reference herein.

The geographic relevancy may also be an input to the quality scoringfunction. In block 1222, geographic relevancy may be an input based onphysical proximity and/or spatial proximity between a user's localintent and an advertisers' geo-targeting intent. Proximity measure maybe multi-dimensional which may effectively encapsulate several factorssuch as business context (e.g. in/out service tendency), local shoppingintent, inventory availability, willingness to drive/walk, and/ortraffic/density conditions. Proximity may be calculated on thesenumerous factors for a given candidate advertisement and user searchquery.

Referring to FIG. 14, an illustrative embodiment of a general computersystem is shown and is designated 1400. Any of the components shown inthe computer system 1400 may describe the components discussed withrespect to FIG. 2 and FIG. 3. For example, the search engine and/or thelocal intent analyzer may include a processor, memory, and/or drive unitas described below. The computer system 1400 can include a set ofinstructions that can be executed to cause the computer system 1400 toperform any one or more of the methods or computer based functionsdisclosed herein. The computer system 1400 may operate as a standalonedevice or may be connected, e.g., using a network, to other computersystems or peripheral devices.

In a networked deployment, the computer system may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 1400 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a mobile device, a palmtop computer, a laptopcomputer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any other machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. In a particularembodiment, the computer system 1400 can be implemented using electronicdevices that provide voice, video or data communication. Further, whilea single computer system 1400 is illustrated, the term “system” shallalso be taken to include any collection of systems or sub-systems thatindividually or jointly execute a set, or multiple sets, of instructionsto perform one or more computer functions.

As illustrated in FIG. 14, the computer system 1400 may include aprocessor 1402, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 1402 may be a component ina variety of systems. For example, the processor 1402 may be part of astandard personal computer or a workstation. The processor 1402 may beone or more general processors, digital signal processors, applicationspecific integrated circuits, field programmable gate arrays, servers,networks, digital circuits, analog circuits, combinations thereof, orother now known or later developed devices for analyzing and processingdata. The processor 1402 may implement a software program, such as codegenerated manually (i.e., programmed).

The computer system 1400 may include a memory 1404 that can communicatevia a bus 1408. The memory 1404 may be a main memory, a static memory,or a dynamic memory. The memory 1404 may include, but is not limited tocomputer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneembodiment, the memory 1404 includes a cache or random access memory forthe processor 1402. In alternative embodiments, the memory 1404 isseparate from the processor 1402, such as a cache memory of a processor,the system memory, or other memory. The memory 1404 may be an externalstorage device or database for storing data. Examples include a harddrive, compact disc (“CD”), digital video disc (“DVD”), memory card,memory stick, floppy disc, universal serial bus (“USB”) memory device,or any other device operative to store data. The memory 1404 is operableto store instructions executable by the processor 1402. The functions,acts or tasks illustrated in the figures or described herein may beperformed by the programmed processor 1402 executing the instructionsstored in the memory 1404. The functions, acts or tasks are independentof the particular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 1400 may further include a display unit1414, such as a liquid crystal display (LCD), an organic light emittingdiode (OLED), a flat panel display, a solid state display, a cathode raytube (CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 1414may act as an interface for the user to see the functioning of theprocessor 1402, or specifically as an interface with the software storedin the memory 1404 or in the drive unit 1406.

Additionally, the computer system 1400 may include an input device 1416configured to allow a user to interact with any of the components ofsystem 1400. The input device 1416 may be a number pad, a keyboard, or acursor control device, such as a mouse, or a joystick, touch screendisplay, remote control or any other device operative to interact withthe system 1400.

In a particular embodiment, as depicted in FIG. 14, the computer system1400 may also include a disk or optical drive unit 1406. The disk driveunit 1406 may include a computer-readable medium 1410 in which one ormore sets of instructions 1412, e.g. software, can be embedded. Further,the instructions 1412 may embody one or more of the methods or logic asdescribed herein. In a particular embodiment, the instructions 1412 mayreside completely, or at least partially, within the memory 1404 and/orwithin the processor 1402 during execution by the computer system 1400.The memory 1404 and the processor 1402 also may includecomputer-readable media as discussed above.

The present disclosure contemplates a computer-readable medium thatincludes instructions 1412 or receives and executes instructions 1412responsive to a propagated signal, so that a device connected to anetwork 1420 can communicate voice, video, audio, images or any otherdata over the network 1420. Further, the instructions 1412 may betransmitted or received over the network 1420 via a communication port1418. The communication port 1418 may be a part of the processor 1402 ormay be a separate component. The communication port 1418 may be createdin software or may be a physical connection in hardware. Thecommunication port 1418 is configured to connect with a network 1420,external media, the display 1414, or any other components in system1400, or combinations thereof. The connection with the network 1420 maybe a physical connection, such as a wired Ethernet connection or may beestablished wirelessly as discussed below. Likewise, the additionalconnections with other components of the system 1400 may be physicalconnections or may be established wirelessly.

The network 1420 may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork 1420 may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols.

While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP)represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b) and is submitted with the understanding that it will not beused to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, various features may begrouped together or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the present invention. Thus, to the maximumextent allowed by law, the scope of the present invention is to bedetermined by the broadest permissible interpretation of the followingclaims and their equivalents, and shall not be restricted or limited bythe foregoing detailed description. While various embodiments of theinvention have been described, it will be apparent to those of ordinaryskill in the art that many more embodiments and implementations arepossible within the scope of the invention. Accordingly, the inventionis not to be restricted except in light of the attached claims and theirequivalents.

1. A method for determining a local intent of a query comprising:receiving a query; identifying a presence of a location term in thequery; separating the query into a root term and the location term whenthe location term is present; analyzing historical searches that recordwhich location terms are most often associated with the root term; andcomputing a local intent probability (LIP) for the root term with aprobability model using the historical searches wherein computing theLIP comprises a probability model using the historical searches, whereinthe probability model is an exponential function between the LIP and alocal intent value (LIV).
 2. The method of claim 1 wherein historicalsearches are broken into root terms that are associated with locationbands, wherein the location bands cover different geographic ranges. 3.The method of claim 2 wherein the LIP is calculated based on whichlocation bands the query root term is associated with.
 4. The method ofclaim 2 wherein the location bands comprise five location bands eachcovering a different geographic range.
 5. The method of claim 4 whereinthe geographic ranges of the five location bands includes a country,state, large city, small city, zip code, neighborhood, or combinationsthereof.
 6. The method of claim 1 wherein the probability model utilizesa truth set from the historical searches.
 7. The method of claim 1further comprising: associating location bands with the root term basedon the location terms in the historical searches that are associatedwith the root term; assigning a value for each of the location bands,wherein the respective values represent a geographic range; multiplyinga frequency the root term is associated with one of the location bandswith the value of the one of the location bands; and combining themultiplied values for the root term to form the LIV.
 8. In a computerreadable storage medium having stored therein data representinginstructions executable by a programmed processor for providing a localintent probability, the storage medium comprising instructions operativeto: receiving a plurality of search queries; separating a root term anda location term from each of the plurality of queries; calculating thelocal intent probability for the root terms from the plurality ofqueries based on the frequency each root term is associated with thelocation terms; categorizing the location terms into location bands eachcovering a different geographic range; computing the frequency in eachof the location bands for the root terms from the plurality of queries;and determining the probability that the root terms appear in each ofthe location bands.
 9. The storage medium of claim 8 wherein each rootterm from the plurality of search queries is associated with at leastone of the location terms.
 10. The storage medium of claim 8 wherein theroot term includes a frequency for each of the location bands, whereinthe frequency in each of the location bands represents a number of timesin the plurality of queries the root term was associated with a locationterm in each of the location bands.
 11. The storage medium of claim 8wherein the location bands comprise at least five location bands. 12.The storage medium of claim 11 wherein the geographic ranges of the fivelocation bands includes a country, state, large city, small city, zipcode, neighborhood, or combinations thereof.
 13. The storage medium ofclaim 8 wherein the local intent probability represents a probabilitythat a search query is geographically dependent.
 14. The storage mediumof claim 8 wherein the local intent probability is calculated from adistribution histogram of the location bands for a particular query. 15.A system determining a local intent probability (LIP) of a search querycomprising: a search engine coupled with a network and configured toreceive the search query over the network; a local intent analyzercoupled with the search engine and configured to extract a root termfrom the search query; and a root term database coupled with the localintent analyzer and configured to store a plurality of root termsextracted from a plurality of historical search queries combined with atleast one location associated with the plurality of historical rootterms; wherein the local intent analyzer determines the local intent ofthe query root term based on the at least one location associated withthe query root term by computing the LIP with a regression model usingthe historical search queries stored in the root term database.
 16. Thesystem according to claim 15 further comprising a matching enginecoupled with the local intent analyzer and configured to compare thequery root term and the local intent of the query root term withavailable content to select relevant content.
 17. The system accordingto claim 16 further comprising a user device coupled with the networkand configured to receive the relevant content over the network inresponse to submitting the search query.
 18. The system according toclaim 15 wherein the local intent is expressed as a local intentprobability that reflects a probability the search query is associatedwith a location.
 19. The system according to claim 15 further comprisingat least one of an internal data source or an external data sourcecoupled with the root term database and configured to provide theplurality of search queries.
 20. The system according to claim 15further comprising a root extractor coupled with the local intentanalyzer and configured to extract the query root term from the searchquery for the local intent analyzer.
 21. The system according to claim15 wherein the regression model comprises an exponential functionbetween the LIP and a local intent value (LIV).